Business Software for Kubernetes

Orchid Security employs a passive listening approach to consistently identify both self-hosted applications, which you oversee, and third-party SaaS applications, offering a thorough inventory of your enterprise's applications alongside critical identity attributes such as multi-factor authentication (MFA) enforcement, the presence of rogue or orphaned accounts, and role-based access control (RBAC) privilege details. By leveraging state-of-the-art AI analytics, Orchid Security automatically evaluates the identity technologies, protocols, and native authentication and authorization processes of each application. The identity controls are then measured against various privacy laws, cybersecurity frameworks, and best practices, including PCI DSS, HIPAA, SOX, GDPR, CMMC, NIST CSF, ISO 27001, and SOC2, in order to identify potential vulnerabilities in your cybersecurity stance and compliance adherence. Not only does Orchid Security provide insights into these vulnerabilities, but it also empowers organizations to swiftly and effectively address these issues without the need for code alterations, thus enhancing overall security posture. This proactive approach ensures that enterprises can maintain compliance while minimizing their risk exposure.

Accelerate your application development and ease database management with the premier hybrid multi-cloud DBaaS that supports Microsoft SQL Server, Oracle, EDB, PostgreSQL, MySQL, MariaDB, and MongoDB. Integrate databases seamlessly into your software development process while minimizing administrative tasks and ensuring optimal performance, resilience, and security for development, testing, and production environments. Provision, clone, and refresh databases effortlessly from development settings, allowing for greater efficiency. Automate administrative functions to maintain consistency with best practices, giving database administrators more time to focus on high-impact activities. Experience dependable performance, scalability, and security across all databases hosted on a hybrid multi-cloud framework. Facilitate collaboration for platform and operations teams by treating databases as code through REST API and Kubernetes integrations. The NDB Operator for Kubernetes empowers developers to provision, clone, and restore databases with minimal code, significantly reducing time-to-market. By leveraging these advanced capabilities, teams can enhance their workflow and drive innovation more effectively than ever before.

The Nutanix Kubernetes Platform (NKP) streamlines platform engineering by minimizing operational challenges and ensuring uniformity across various environments. It offers all the necessary elements for a production-ready Kubernetes setup within a fully integrated, turnkey framework. You can deploy it in public cloud settings, on-premises, or at edge locations, with or without the Nutanix Cloud Infrastructure. The platform is built from upstream CNCF projects that are not only fully integrated and validated but also easily replaceable, preventing vendor lock-in. It simplifies the management of complex microservices while improving observability and security. Additionally, it provides robust multi-cluster management features for your public cloud Kubernetes deployments without necessitating a shift to a different runtime. By harnessing the power of AI, it helps users maximize their Kubernetes experience through anomaly detection paired with root cause analysis, as well as an intelligent chatbot that offers best practices and fosters consistency in operations. This comprehensive approach enables teams to focus more on innovation rather than being bogged down by operational hurdles.

Nutanix Enterprise AI makes it simple to deploy, operate, and develop enterprise AI applications through secure AI endpoints that utilize large language models and generative AI APIs. By streamlining the process of integrating GenAI, Nutanix enables organizations to unlock extraordinary productivity boosts, enhance revenue streams, and realize the full potential of generative AI. With user-friendly workflows, you can effectively monitor and manage AI endpoints, allowing you to tap into your organization's AI capabilities. The platform's point-and-click interface facilitates the effortless deployment of AI models and secure APIs, giving you the flexibility to select from Hugging Face, NVIDIA NIM, or your customized private models. You have the option to run enterprise AI securely, whether on-premises or in public cloud environments, all while utilizing your existing AI tools. The system also allows for straightforward management of access to your language models through role-based access controls and secure API tokens designed for developers and GenAI application owners. Additionally, with just a single click, you can generate URL-ready JSON code, making API testing quick and efficient. This comprehensive approach ensures that enterprises can fully leverage their AI investments and adapt to evolving technological landscapes seamlessly.

No expertise required! Our Kubernetes Management platform allows you to create clusters in minutes. Every feature of our platform has been designed to automate DevOps. We ensure that every component is tightly interconnected by building everything from scratch. This allows us to achieve the best performance and reduce complexity. Syself Autopilot supports declarative configurations. This is an approach where configuration files are used to define the desired states of your infrastructure and application. Instead of issuing commands that change the current state, the system will automatically make the necessary adjustments in order to achieve the desired state.

Cloudfleet provides a Kubernetes experience that spans from datacenters to the cloud and edge, ensuring it meets its intended purpose. With just-in-time infrastructure, automated updates, and sophisticated permissions management, users can effortlessly oversee their clusters through a unified interface. As a comprehensive multi-cloud and hybrid Kubernetes solution, Cloudfleet streamlines the setup of your infrastructure by enabling automatic server provisioning across both on-premises settings and a dozen different cloud service providers, enhancing efficiency and flexibility for your operations. This approach not only minimizes the complexity of managing diverse environments but also empowers users to focus more on their core objectives.

Pipeshift is an adaptable orchestration platform developed to streamline the creation, deployment, and scaling of open-source AI components like embeddings, vector databases, and various models for language, vision, and audio, whether in cloud environments or on-premises settings. It provides comprehensive orchestration capabilities, ensuring smooth integration and oversight of AI workloads while being fully cloud-agnostic, thus allowing users greater freedom in their deployment choices. Designed with enterprise-level security features, Pipeshift caters specifically to the demands of DevOps and MLOps teams who seek to implement robust production pipelines internally, as opposed to relying on experimental API services that might not prioritize privacy. Among its notable functionalities are an enterprise MLOps dashboard for overseeing multiple AI workloads, including fine-tuning, distillation, and deployment processes; multi-cloud orchestration equipped with automatic scaling, load balancing, and scheduling mechanisms for AI models; and effective management of Kubernetes clusters. Furthermore, Pipeshift enhances collaboration among teams by providing tools that facilitate the monitoring and adjustment of AI models in real-time.

Token Security presents an innovative strategy tailored for the booming era of Non-Human Identities (NHI), emphasizing a machine-first approach to identity security. In today's digital landscape, identities are omnipresent and often unmanaged; they manifest as machines, applications, services, and workloads, continuously generated by various sources throughout the day. The intricate and sluggish nature of managing these identities has resulted in an attack surface that organizations find difficult to navigate. Rather than concentrating solely on human identities, Token prioritizes the resources being accessed, swiftly revealing who accesses which resources, identifying vulnerabilities, and ensuring security without disrupting operations. Furthermore, Token adeptly identifies all identities across cloud environments, seamlessly integrating intricate components such as Kubernetes, databases, servers, and containers, thereby consolidating relevant identity data into a cohesive perspective. This comprehensive approach not only enhances security but also simplifies the management of identities within increasingly complex infrastructures.

O-mega stands out as the first-ever productivity platform tailored specifically for multi-agent teams, empowering organizations to create AI agents that can operate independently. These intelligent agents are engineered to perform actions safely and judiciously, understanding the appropriate tools and conditions necessary for task completion. They work seamlessly across various processes, departments, roles, and levels of authorization, all while maintaining an awareness of the organization's mission, guidelines, and industry regulations. O-mega provides universal connectivity for agents to engage with any platform, API, web browser, or legacy system, such as Slack, GitHub, Dropbox, Google, Microsoft, AWS, Shopify, Salesforce, Stripe, WordPress, LinkedIn, Twitter, YouTube, Discord, Apple, WhatsApp, and many others. This extensive connectivity facilitates the automation of a wide range of business processes through agentic process automation, enabling AI agents to manage tasks that include writing and publishing content, processing invoices, onboarding new team members, and creating weekly financial summaries. Ultimately, O-mega redefines efficiency by allowing organizations to leverage AI for streamlined operations and enhanced productivity.

Kubiya is an innovative internal developer platform powered by AI that utilizes conversational technology to optimize DevOps workflows. By seamlessly connecting with existing tools and platforms, it enables developers to engage with their systems through natural language, significantly decreasing the time required for automation and boosting overall productivity. This platform features AI-driven assistants that can efficiently manage routine functions such as overseeing Jira queues, provisioning infrastructure, and applying just-in-time cloud permissions, thereby empowering engineering teams to concentrate on more strategic tasks. With its agentic-native architecture, Kubiya guarantees reliable and secure operations, adhering to rigorous enterprise security standards and compliance with corporate policies. Additionally, the platform integrates effortlessly into communication tools, including Slack and Microsoft Teams, offering a user-friendly conversational interface for task delegation and automation. As a result, Kubiya not only enhances efficiency but also fosters a more collaborative environment for development teams.

ZEST Security presents an innovative platform for risk resolution that harnesses the power of AI to transform cloud risk remediation specifically for security teams. In contrast to conventional security measures that simply point out vulnerabilities, ZEST takes a proactive approach by linking the appropriate team to the necessary solutions, thus shortening the duration from vulnerability identification to effective remediation. This comprehensive platform ensures full remediation coverage by juxtaposing the intended DevOps configuration with the current cloud runtime environment, facilitating effortless detection and addressing of risks in both managed and unmanaged cloud settings. With automated root cause analysis, the platform accurately identifies the source of issues, down to specific assets and lines of code, enabling teams to tackle multiple challenges with minimal adjustments. Furthermore, the AI-driven risk resolution pathways significantly shorten the mean time to remediation and remove the need for manual triage through the application of dynamic remediation techniques. As a result, security teams can respond to threats more swiftly and efficiently than ever before.

The Qvera Interface Engine (QIE) serves as a robust integration platform within the healthcare sector, aimed at enhancing interoperability among diverse healthcare systems. It accommodates an extensive array of standards, such as HL7, FHIR, DICOM, CDA, JSON, CSV, XML, and X12, which promotes effective data sharing among electronic health records, imaging systems, and health information exchanges. QIE provides versatile deployment options, permitting installations either on-premises or within cloud environments like AWS, Azure, Google Cloud Platform, and IBM Cloud. With its user-friendly drag-and-drop visual channel editor, the platform streamlines the process of developing and managing interfaces, making it accessible for users. The engine guarantees high availability with an impressive uptime of 99.999% and adheres to SOC 2 Type 2 compliance, reflecting its dedication to both security and reliability. Additionally, QIE's capabilities empower healthcare organizations to adapt swiftly to evolving technological demands and regulatory standards.

Open WebUI is a robust, user-friendly, and customizable AI platform that is self-hosted and capable of functioning entirely without an internet connection. It is compatible with various LLM runners, such as Ollama, alongside APIs that align with OpenAI standards, and features an integrated inference engine that supports Retrieval Augmented Generation (RAG), positioning it as a formidable choice for AI deployment. Notable aspects include an easy installation process through Docker or Kubernetes, smooth integration with OpenAI-compatible APIs, detailed permissions, and user group management to bolster security, as well as a design that adapts well to different devices and comprehensive support for Markdown and LaTeX. Furthermore, Open WebUI presents a Progressive Web App (PWA) option for mobile usage, granting users offline access and an experience akin to native applications. The platform also incorporates a Model Builder, empowering users to develop tailored models from base Ollama models directly within the system. With a community of over 156,000 users, Open WebUI serves as a flexible and secure solution for the deployment and administration of AI models, making it an excellent choice for both individuals and organizations seeking offline capabilities. Its continuous updates and feature enhancements only add to its appeal in the ever-evolving landscape of AI technology.

Pelanor is an innovative FinOps platform that leverages AI to cater to the needs of contemporary cloud environments, ensuring seamless integration across various cloud systems to illuminate organizational cloud expenditures. By providing contextual cost insights, it equips engineering, finance, and business leaders with the necessary tools to make well-informed choices. The platform identifies actionable opportunities by addressing major cost drivers and promptly resolving cost-related incidents before they worsen. Pelanor enhances understanding of how cloud spending correlates with business metrics, granting a holistic view of the factors influencing expenses and ways to optimize them. Its sophisticated cost allocation features promote accountability throughout the organization, allowing for accurate chargebacks. Furthermore, Pelanor's autonomous AI-driven approach simplifies cloud spending management and facilitates informed decision-making for leaders. The platform ensures comprehensive visibility into costs at intricate levels, such as individual queries or job executions, while offering tailored anomaly detection systems and detailed alerts. This level of granularity not only aids in precise financial planning but also helps organizations to proactively manage their cloud resources effectively.

vLLM is an advanced library tailored for the efficient inference and deployment of Large Language Models (LLMs). Initially created at the Sky Computing Lab at UC Berkeley, it has grown into a collaborative initiative enriched by contributions from both academic and industry sectors. The library excels in providing exceptional serving throughput by effectively handling attention key and value memory through its innovative PagedAttention mechanism. It accommodates continuous batching of incoming requests and employs optimized CUDA kernels, integrating technologies like FlashAttention and FlashInfer to significantly improve the speed of model execution. Furthermore, vLLM supports various quantization methods, including GPTQ, AWQ, INT4, INT8, and FP8, and incorporates speculative decoding features. Users enjoy a seamless experience by integrating easily with popular Hugging Face models and benefit from a variety of decoding algorithms, such as parallel sampling and beam search. Additionally, vLLM is designed to be compatible with a wide range of hardware, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, ensuring flexibility and accessibility for developers across different platforms. This broad compatibility makes vLLM a versatile choice for those looking to implement LLMs efficiently in diverse environments.

Crusoe delivers a cloud infrastructure tailored for artificial intelligence tasks, equipped with cutting-edge GPU capabilities and top-tier data centers. This platform is engineered for AI-centric computing, showcasing high-density racks alongside innovative direct liquid-to-chip cooling to enhance overall performance. Crusoe’s infrastructure guarantees dependable and scalable AI solutions through features like automated node swapping and comprehensive monitoring, complemented by a dedicated customer success team that assists enterprises in rolling out production-level AI workloads. Furthermore, Crusoe emphasizes environmental sustainability by utilizing clean, renewable energy sources, which enables them to offer economical services at competitive pricing. With a commitment to excellence, Crusoe continuously evolves its offerings to meet the dynamic needs of the AI landscape.

MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.

Mammoth Cyber's Enterprise Browser is a Chromium-based tool crafted to improve secure remote access by embedding a policy engine within the browser itself. This solution provides organizations with the ability to monitor and manage user interactions across internal applications, public cloud services, and SaaS platforms effectively. By enforcing conditional access and adhering to least privilege principles, it guarantees that users can only access the resources pertinent to their specific roles, thus significantly mitigating the chances of data breaches. Comprehensive audit logs of user activities bolster compliance efforts and enhance security oversight. The Enterprise Browser works effortlessly with identity providers such as Okta and Azure AD, which automates role-based permissions and simplifies the onboarding process for users. With an interface that users find familiar, the browser ensures a smooth transition and encourages widespread adoption. Furthermore, it enables secure developer access by supporting connections via SSH, RDP, Git, Kubernetes, and databases directly, enhancing the overall efficiency for technical teams. This multifaceted approach not only prioritizes security but also promotes a productive work environment.

Observo AI is an innovative platform tailored for managing large-scale telemetry data within security and DevOps environments. Utilizing advanced machine learning techniques and agentic AI, it automates the optimization of data, allowing companies to handle AI-generated information in a manner that is not only more efficient but also secure and budget-friendly. The platform claims to cut data processing expenses by over 50%, while improving incident response speeds by upwards of 40%. Among its capabilities are smart data deduplication and compression, real-time anomaly detection, and the intelligent routing of data to suitable storage or analytical tools. Additionally, it enhances data streams with contextual insights, which boosts the accuracy of threat detection and helps reduce the occurrence of false positives. Observo AI also features a cloud-based searchable data lake that streamlines data storage and retrieval, making it easier for organizations to access critical information when needed. This comprehensive approach ensures that enterprises can keep pace with the evolving landscape of cybersecurity threats.

NVIDIA's DeepStream SDK serves as a robust toolkit for streaming analytics, leveraging GStreamer to facilitate AI-driven processing across various sensors, including video, audio, and image data. It empowers developers to craft intricate stream-processing pipelines that seamlessly integrate neural networks alongside advanced functionalities like tracking, video encoding and decoding, as well as rendering, thereby enabling real-time analysis of diverse data formats. DeepStream plays a crucial role within NVIDIA Metropolis, a comprehensive platform aimed at converting pixel and sensor information into practical insights. This SDK presents a versatile and dynamic environment catered to multiple sectors, offering support for an array of programming languages such as C/C++, Python, and an easy-to-use UI through Graph Composer. By enabling real-time comprehension of complex, multi-modal sensor information at the edge, it enhances operational efficiency while also providing managed AI services that can be deployed in cloud-native containers managed by Kubernetes. As industries increasingly rely on AI for decision-making, DeepStream's capabilities become even more vital in unlocking the value embedded within sensor data.

NVIDIA Cloud Functions (NVCF) is a serverless API tailored for deploying and managing AI tasks on GPUs, ensuring security, scalability, and dependable performance. It accommodates various access methods, including HTTP polling, HTTP streaming, and gRPC protocols, for interacting with workloads. Primarily, Cloud Functions is optimized for brief, preemptable tasks such as inferencing and model fine-tuning. Users can choose between two types of functions: "Container" and "Helm Chart," enabling them to customize functions according to their specific needs. Since workloads are transient and preemptable, it is crucial for users to save their progress diligently. Additionally, models, containers, helm charts, and other essential resources are stored and retrieved from the NGC Private Registry. To begin utilizing NVCF, users can refer to the quickstart guide for functions, which outlines a comprehensive workflow for establishing and launching a container-based function utilizing the fastapi_echo_sample container. This resource not only highlights the ease of setup but also encourages users to explore the full potential of NVIDIA’s serverless infrastructure.

The Qualcomm AI Inference Suite serves as a robust software platform aimed at simplifying the implementation of AI models and applications in both cloud-based and on-premises settings. With its convenient one-click deployment feature, users can effortlessly incorporate their own models, which can include generative AI, computer vision, and natural language processing, while also developing tailored applications that utilize widely-used frameworks. This suite accommodates a vast array of AI applications, encompassing chatbots, AI agents, retrieval-augmented generation (RAG), summarization, image generation, real-time translation, transcription, and even code development tasks. Enhanced by Qualcomm Cloud AI accelerators, the platform guarantees exceptional performance and cost-effectiveness, thanks to its integrated optimization methods and cutting-edge models. Furthermore, the suite is built with a focus on high availability and stringent data privacy standards, ensuring that all model inputs and outputs remain unrecorded, thereby delivering enterprise-level security and peace of mind to users. Overall, this innovative platform empowers organizations to maximize their AI capabilities while maintaining a strong commitment to data protection.

Cleric serves as an independent AI Site Reliability Engineer (SRE) that autonomously oversees, optimizes, and repairs software infrastructure without the need for human oversight. Acting as a collaborative AI partner, it seamlessly integrates with various existing tools, such as Kubernetes, Datadog, Prometheus, and Slack, to explore and diagnose production issues. By automatically managing alerts, Cleric enables engineers to dedicate more time to development rather than routine tasks. It efficiently evaluates systems simultaneously, providing insights in mere minutes, which would typically take hours to resolve manually. When faced with unfamiliar problems, Cleric formulates hypotheses and executes real-time queries with its integrated tools, only presenting conclusions once it is confident in its findings. With each investigation, Cleric enhances its capabilities by learning from actual outcomes and incidents. By the end of the first month, Cleric is equipped to manage approximately 20–30% of on-call responsibilities, empowering your team to prioritize problem-solving over monotonous alert triage. As a result, the overall efficiency and productivity of the engineering team can significantly improve.

TIBCO Streaming is an advanced analytics platform focused on real-time processing and analysis of fast-moving data streams, which empowers organizations to make swift, data-informed choices. With its low-code development environment found in StreamBase Studio, users can create intricate event processing applications with ease and minimal coding requirements. The platform boasts compatibility with over 150 connectors, such as APIs, Apache Kafka, MQTT, RabbitMQ, and databases like MySQL and JDBC, ensuring smooth integration with diverse data sources. Incorporating dynamic learning operators, TIBCO Streaming allows for the use of adaptive machine learning models that deliver contextual insights and enhance automation in decision-making. Additionally, it provides robust real-time business intelligence features that enable users to visualize current data alongside historical datasets for a thorough analysis. The platform is also designed for cloud readiness, offering deployment options across AWS, Azure, GCP, and on-premises setups, thereby ensuring flexibility for various organizational needs. Overall, TIBCO Streaming stands out as a powerful solution for businesses aiming to harness real-time data for strategic advantages.

MasterDC, an IT infrastructure provider based in the Czech Republic, excels in managed services, cloud solutions, and high-quality hosting for enterprise clients. With modern data centers located in both Prague and Brno, the company has been a trusted partner for businesses and organizations for almost thirty years. Its extensive range of services includes dedicated servers, VPS hosting, cloud servers, server housing, and various managed services. MasterDC is equipped to handle a variety of technologies such as Kubernetes, Docker, Ceph, Hyper-V, and Proxmox VE, addressing a wide array of infrastructure requirements. The company also prioritizes strong networking and security measures, offering solutions like AWS Direct Connect, firewall protection, DDoS mitigation, log management, and SIEM services to ensure client safety. In terms of data storage, MasterDC provides options such as Nextcloud hosting, data replication, and server backup, making it easier for organizations to manage their information. Their database solutions include database as a service, Elasticsearch, Galera Cluster, and optimization services, ensuring efficient data handling and performance. Overall, MasterDC continues to adapt and innovate in the ever-evolving technology landscape to meet their clients' needs.